import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
_SCREAMING_SNAKE_CASE 		=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 for attribute in key.split("""."""       ):
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					UpperCamelCase_       )

 if weight_type is not None:
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					UpperCamelCase_       ).shape
 else:
  UpperCamelCase			  =      hf_pointer.shape

 assert hf_shape == value.shape, (
     f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
     f""" {value.shape} for {full_name}"""
 )

 if weight_type == "weight":
  UpperCamelCase			  =      value
 elif weight_type == "weight_g":
  UpperCamelCase			  =      value
 elif weight_type == "weight_v":
  UpperCamelCase			  =      value
 elif weight_type == "bias":
  UpperCamelCase			  =      value
 else:
  UpperCamelCase			  =      value

 logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      []
 UpperCamelCase			  =      fairseq_model.state_dict()

 UpperCamelCase			  =      hf_model.feature_extractor

 # if encoder has different dim to decoder -> use proj_weight
 UpperCamelCase			  =      None

 for name, value in fairseq_dict.items():
  UpperCamelCase			  =      False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					hf_model.config.feat_extract_norm == """group"""							,					)
   UpperCamelCase			  =      True
  elif name.split("""."""       )[0] == "proj":
   UpperCamelCase			  =      fairseq_model.proj
   UpperCamelCase			  =      True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
     UpperCamelCase			  =      True
     if "*" in mapped_key:
      UpperCamelCase			  =      name.split(UpperCamelCase_       )[0].split("""."""       )[-2]
      UpperCamelCase			  =      mapped_key.replace("""*"""							,					UpperCamelCase_       )
     if "weight_g" in name:
      UpperCamelCase			  =      """weight_g"""
     elif "weight_v" in name:
      UpperCamelCase			  =      """weight_v"""
     elif "bias" in name:
      UpperCamelCase			  =      """bias"""
     elif "weight" in name:
      UpperCamelCase			  =      """weight"""
     else:
      UpperCamelCase			  =      None
     set_recursively(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
    continue
  if not is_used:
   unused_weights.append(UpperCamelCase_       )

 logger.warning(f"""Unused weights: {unused_weights}"""       )

 return proj_weight





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      full_name.split("""conv_layers."""       )[-1]
 UpperCamelCase			  =      name.split("""."""       )
 UpperCamelCase			  =      int(items[0]       )
 UpperCamelCase			  =      int(items[1]       )

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f"""{full_name} has size {value.shape}, but"""
       f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
   )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f"""{full_name} has size {value.shape}, but"""
       f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
   )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
       " found."
   )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f"""{full_name} has size {value.shape}, but"""
       f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
   )
   UpperCamelCase			  =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
 else:
  unused_weights.append(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 UpperCamelCase  ,     UpperCamelCase			  =      emb.weight.shape
 UpperCamelCase			  =      nn.Linear(UpperCamelCase_							,					UpperCamelCase_							,					bias=UpperCamelCase_       )
 UpperCamelCase			  =      emb.weight.data
 return lin_layer





def 			lowercase(	UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 with open(UpperCamelCase_							,					"""r"""							,					encoding="""utf-8"""       ) as f:
  UpperCamelCase			  =      f.readlines()
  UpperCamelCase			  =      [line.split(""" """       )[0] for line in lines]

 UpperCamelCase			  =      len(UpperCamelCase_       )

 UpperCamelCase			  =      {
     """<s>""": 0,
     """<pad>""": 1,
     """</s>""": 2,
     """<unk>""": 3,
 }

 vocab_dict.update(dict(zip(UpperCamelCase_							,					range(4							,					num_words + 4       )       )       )       )
 return vocab_dict





@torch.no_grad()
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					)		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      WavaVecaConfig.from_pretrained(UpperCamelCase_       )
 UpperCamelCase			  =      SpeechaTextaConfig.from_pretrained(
     UpperCamelCase_							,					vocab_size=UpperCamelCase_							,					decoder_layers=UpperCamelCase_							,					do_stable_layer_norm=UpperCamelCase_       )

 UpperCamelCase			  =      WavaVecaFeatureExtractor(
     feature_size=1							,					sampling_rate=16000							,					padding_value=0							,					do_normalize=UpperCamelCase_							,					return_attention_mask=UpperCamelCase_							,					)

 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]							,					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
 UpperCamelCase			  =      model[0].eval()

 # set weights for wav2vec2 encoder
 UpperCamelCase			  =      WavaVecaModel(UpperCamelCase_       )
 UpperCamelCase			  =      recursively_load_weights_wavaveca(model.encoder							,					UpperCamelCase_       )

 UpperCamelCase			  =      SpeechaTextaForCausalLM(UpperCamelCase_       )
 UpperCamelCase  ,     UpperCamelCase			  =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,					strict=UpperCamelCase_       )

 # set output linear layer
 unexpected_keys.remove("""embed_out"""       )
 UpperCamelCase			  =      nn.Parameter(model.decoder.embed_out.detach()       )

 # layer norm is init to identity matrix so leaving it is fine
 logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""       )
 logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""       )

 UpperCamelCase			  =      SpeechEncoderDecoderModel(encoder=UpperCamelCase_							,					decoder=UpperCamelCase_       )
 UpperCamelCase			  =      False

 # add projection layer
 UpperCamelCase			  =      nn.Parameter(projection_layer.weight       )
 UpperCamelCase			  =      nn.Parameter(projection_layer.bias       )

 UpperCamelCase			  =      create_vocab_dict(UpperCamelCase_       )

 with open(os.path.join(UpperCamelCase_							,					"""vocab.json"""       )							,					"""w"""       ) as fp:
  json.dump(UpperCamelCase_							,					UpperCamelCase_       )

 UpperCamelCase			  =      SpeechaTextaTokenizer(os.path.join(UpperCamelCase_							,					"""vocab.json"""       )       )
 tokenizer.save_pretrained(UpperCamelCase_       )

 UpperCamelCase			  =      hf_wavavec.config.to_dict()
 UpperCamelCase			  =      tokenizer.pad_token_id
 UpperCamelCase			  =      tokenizer.bos_token_id
 UpperCamelCase			  =      tokenizer.eos_token_id
 UpperCamelCase			  =      """speech_to_text_2"""
 UpperCamelCase			  =      """wav2vec2"""

 UpperCamelCase			  =      SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_       )

 hf_wavavec.save_pretrained(UpperCamelCase_       )
 feature_extractor.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument(
         """--encoder_config_path""",
         default="""facebook/wav2vec2-large-lv60""",
         type=str,
         help="""Path to hf encoder wav2vec2 checkpoint config""",
     )
     parser.add_argument(
         """--decoder_config_path""",
         default="""facebook/s2t-small-mustc-en-fr-st""",
         type=str,
         help="""Path to hf decoder s2t checkpoint config""",
     )
     parser.add_argument("""--vocab_size""", default=1_0_2_2_4, type=int, help="""Vocab size of decoder""")
     parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.dict_path,
         encoder_config_path=args.encoder_config_path,
         decoder_config_path=args.decoder_config_path,
         vocab_size=args.vocab_size,
         num_decoder_layers=args.num_decoder_layers,
     )

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
